# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Tokenization classes for fast tokenizers (provided by HuggingFace's tokenizers library). For slow (python) tokenizers
see tokenization_utils.py
"""
import copy
import json
import os
from collections import defaultdict
from typing import Any, Dict, List, Optional, Tuple, Union
import tokenizers.pre_tokenizers as pre_tokenizers_fast
from tokenizers import Encoding as EncodingFast
from tokenizers import Tokenizer as TokenizerFast
from tokenizers.decoders import Decoder as DecoderFast
from tokenizers.trainers import BpeTrainer, UnigramTrainer, WordLevelTrainer, WordPieceTrainer
from mindformers.tools.utils import FILE_PERMISSION
from .convert_slow_tokenizer import convert_slow_tokenizer
from .tokenization_utils import PreTrainedTokenizer
from .tokenization_utils_base import (
AddedToken,
BatchEncoding,
PreTokenizedInput,
PreTokenizedInputPair,
PreTrainedTokenizerBase,
SpecialTokensMixin,
TextInput,
TextInputPair,
TruncationStrategy,
PaddingStrategy
)
# Fast tokenizers (provided by HuggingFace tokenizer's library) can be saved in a single file
TOKENIZER_FILE = "tokenizer.json"
SPECIAL_TOKENS_MAP_FILE = "special_tokens_map.json"
TOKENIZER_CONFIG_FILE = "tokenizer_config.json"
# Slow tokenizers have an additional added tokens files
ADDED_TOKENS_FILE = "added_tokens.json"
MODEL_TO_TRAINER_MAPPING = {
"BPE": BpeTrainer,
"Unigram": UnigramTrainer,
"WordLevel": WordLevelTrainer,
"WordPiece": WordPieceTrainer,
}
VOCAB_FILES_NAMES = {"tokenizer_file": TOKENIZER_FILE}
# pylint: disable=W0223
[docs]class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
r"""
Base class for all fast tokenizers (wrapping HuggingFace tokenizers library).
Handles all the shared methods for tokenization and special tokens, as well as methods for
downloading/caching/loading pretrained tokenizers, as well as adding tokens to the vocabulary.
This class also contains the added tokens in a unified way on top of all tokenizers so we don't have to handle the
specific vocabulary augmentation methods of the various underlying dictionary structures (BPE, sentencepiece...).
Args:
**kwargs (Any): Keyword arguments.
- model_max_length (int, optional):
The maximum length (in number of tokens) for the inputs to the transformer model.
Set when the tokenizer is loaded with ``from_pretrained()`` based on the model's
``max_model_input_sizes`` attribute. Default: ``1e-30``.
- padding_side (str, optional):
Specifies the side on which the model should have padding applied. Options are
['right', 'left']. The default value is picked from the class attribute of the
same name.
- truncation_side (str, optional):
Specifies the side on which the model should have truncation applied.
Options are ['right', 'left']. The default value is picked from the class
attribute of the same name.
- chat_template (str, optional):
A Jinja template string used to format lists of chat messages.
Default: ``"{% for message in messages %}{{'<|im_start|>' + message['role'] +
'\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{
'<|im_start|>assistant\n' }}{% endif %}"``.
- model_input_names (List[str], optional):
Lists the names of inputs accepted by the forward pass of the model,
such as "token_type_ids" or "attention_mask". Defaults to values picked
from the class attribute of the same name. Default: ``None``.
- bos_token (Union[str, tokenizers.AddedToken], optional):
Represents the beginning of a sentence and is associated with
``self.bos_token`` and ``self.bos_token_id``. Default: ``None``.
- eos_token (Union[str, tokenizers.AddedToken], optional):
Represents the end of a sentence and is associated with ``self.eos_token``
and ``self.eos_token_id``. Default: ``None``.
- unk_token (Union[str, tokenizers.AddedToken], optional):
Represents an out-of-vocabulary token and is associated with
``self.unk_token`` and ``self.unk_token_id``. Default: ``None``.
- sep_token (Union[str, tokenizers.AddedToken], optional):
A special token separating two different sentences in the same input
(used by BERT, for example) and is associated with ``self.sep_token``
and ``self.sep_token_id``. Default: ``None``.
- pad_token (Union[str, tokenizers.AddedToken], optional):
Used to make arrays of tokens the same size for batching purposes and
will be ignored by attention mechanisms or loss computation. It is
associated with ``self.pad_token`` and ``self.pad_token_id``. Default: ``None``.
- cls_token (Union[str, tokenizers.AddedToken], optional):
Represents the class of the input (used by BERT, for example) and is
associated with ``self.cls_token`` and ``self.cls_token_id``. Default: ``None``.
- mask_token (Union[str, tokenizers.AddedToken], optional):
Represents a masked token (used by masked-language modeling pretraining
objectives like BERT) and is associated with ``self.mask_token`` and
``self.mask_token_id``. Default: ``None``.
- additional_special_tokens (Union[tuple, list, tokenizers.AddedToken], optional):
Lists additional special tokens that are ensured to be skipped when
decoding with ``skip_special_tokens`` set to True. They will be added
at the end of the vocabulary if not already part of it. Default: ``None``.
- clean_up_tokenization_spaces (bool, optional):
Determines whether to clean-up spaces that were added when splitting the
input text during the tokenization process. Default: ``True``.
- split_special_tokens (bool, optional):
Specifies whether special tokens should be split during the tokenization
process. This affects the internal state of the tokenizer. By default, special
tokens are not split. For example, if ``<s>`` is the ``bos_token``, then
``tokenizer.tokenize("<s>") = ['<s>']``. If ``split_special_tokens = True``,
then ``tokenizer.tokenize("<s>")`` would result in ``['<','s', '>']``. Default: ``False``.
- tokenizer_object (tokenizers.Tokenizer):
A ``tokenizers.Tokenizer`` object from tokenizers to instantiate from.
- tokenizer_file (str):
A path to a local JSON file representing a previously serialized ``tokenizers.Tokenizer`` object
from tokenizers.
Returns:
PreTrainedTokenizerFast instance.
"""
vocab_files_names = VOCAB_FILES_NAMES
slow_tokenizer_class: PreTrainedTokenizer = None
def __init__(self, *args, **kwargs):
fast_tokenizer, slow_tokenizer, added_tokens_decoder, kwargs = self.init_atom_1(*args, **kwargs)
self._tokenizer = fast_tokenizer
if slow_tokenizer is not None:
kwargs.update(slow_tokenizer.init_kwargs)
self._decode_use_source_tokenizer = False
kwargs = self.init_atom_2(**kwargs)
# We call this after having initialized the backend tokenizer because we update it.
super().__init__(**kwargs)
# The following logic will be replaced with a single add_tokens once a fix is pushed to tokenizers
# allows converting a slow -> fast, non-legacy: if the `tokenizer.json` does not have all the added tokens
# uses the information stored in `added_tokens_decoder`.
# this is costly for fast tokenizers as we re-compute the regex again. But not all tokens are added tokens
tokens_to_add = [
token
for index, token in sorted(added_tokens_decoder.items(), key=lambda x: x[0])
if token not in self.added_tokens_decoder
]
encoder = list(self.added_tokens_encoder.keys()) + [str(token) for token in tokens_to_add]
# if some of the special tokens are strings, we check if we don't already have a token
tokens_to_add += [
token for token in self.all_special_tokens_extended if token not in encoder and token not in tokens_to_add
]
if tokens_to_add:
# super hack: if a token.special is set, tokenizer ignores it for now so FIXME @ArthurZ
# Accumulate added tokens into batches of special/non-special tokens, because calling add_tokens() for
# individual tokens would repeatedly rebuild a trie, which can be slow.
is_last_special = None
tokens = []
special_tokens = self.all_special_tokens
for token in tokens_to_add:
is_special = (
(token.special or str(token) in special_tokens)
if isinstance(token, AddedToken)
else str(token) in special_tokens
)
if is_last_special is None or is_last_special == is_special:
tokens.append(token)
else:
self._add_tokens(tokens, special_tokens=is_last_special)
tokens = [token]
is_last_special = is_special
if tokens:
self._add_tokens(tokens, special_tokens=is_last_special)
def init_atom_1(self, *args, **kwargs):
"""init atom 1"""
tokenizer_object = kwargs.pop("tokenizer_object", None)
slow_tokenizer = kwargs.pop("__slow_tokenizer", None)
fast_tokenizer_file = kwargs.pop("tokenizer_file", None)
from_slow = kwargs.pop("from_slow", False)
added_tokens_decoder = kwargs.pop("added_tokens_decoder", {})
if from_slow and slow_tokenizer is None and self.slow_tokenizer_class is None:
raise ValueError(
"Cannot instantiate this tokenizer from a slow version. If it's based on sentencepiece, make sure you "
"have sentencepiece installed."
)
if tokenizer_object is not None:
fast_tokenizer = copy.deepcopy(tokenizer_object)
elif fast_tokenizer_file is not None and not from_slow:
# We have a serialization from tokenizers which let us directly build the backend
fast_tokenizer_file = os.path.realpath(fast_tokenizer_file)
if os.path.isfile(fast_tokenizer_file) and os.access(fast_tokenizer_file, os.R_OK):
fast_tokenizer = TokenizerFast.from_file(fast_tokenizer_file)
else:
raise ValueError(f"The path of fast_tokenizer_file is {fast_tokenizer_file}, "
"but it is not a valid file.")
elif slow_tokenizer is not None:
# We need to convert a slow tokenizer to build the backend
fast_tokenizer = convert_slow_tokenizer(slow_tokenizer)
elif self.slow_tokenizer_class is not None:
# We need to create and convert a slow tokenizer to build the backend
# pylint: disable=E1102
slow_tokenizer = self.slow_tokenizer_class(*args, **kwargs)
fast_tokenizer = convert_slow_tokenizer(slow_tokenizer)
else:
raise ValueError(
"Couldn't instantiate the backend tokenizer from one of: \n"
"(1) a `tokenizers` library serialization file, \n"
"(2) a slow tokenizer instance to convert or \n"
"(3) an equivalent slow tokenizer class to instantiate and convert. \n"
"You need to have sentencepiece installed to convert a slow tokenizer to a fast one."
)
return fast_tokenizer, slow_tokenizer, added_tokens_decoder, kwargs
def init_atom_2(self, **kwargs):
"""init atom 2"""
truncation = self._tokenizer.truncation
if truncation is not None:
self._tokenizer.enable_truncation(**truncation)
kwargs.setdefault("max_length", truncation["max_length"])
kwargs.setdefault("truncation_side", truncation["direction"])
kwargs.setdefault("stride", truncation["stride"])
kwargs.setdefault("truncation_strategy", truncation["strategy"])
else:
self._tokenizer.no_truncation()
padding = self._tokenizer.padding
if padding is not None:
self._tokenizer.enable_padding(**padding)
kwargs.setdefault("pad_token", padding["pad_token"])
kwargs.setdefault("pad_token_type_id", padding["pad_type_id"])
kwargs.setdefault("padding_side", padding["direction"])
kwargs.setdefault("max_length", padding["length"])
kwargs.setdefault("pad_to_multiple_of", padding["pad_to_multiple_of"])
return kwargs
@property
def is_fast(self) -> bool:
return True
@property
def can_save_slow_tokenizer(self) -> bool:
"""
`bool`, Whether or not the slow tokenizer can be saved. Usually for sentencepiece based slow tokenizer, this
can only be `True` if the original `"sentencepiece.model"` was not deleted.
"""
return True
@property
def vocab_size(self) -> int:
"""
`int`, Size of the base vocabulary (without the added tokens).
"""
return self._tokenizer.get_vocab_size(with_added_tokens=False)
def get_vocab(self) -> Dict[str, int]:
return self._tokenizer.get_vocab(with_added_tokens=True)
@property
def vocab(self) -> Dict[str, int]:
return self.get_vocab()
@property
def added_tokens_encoder(self) -> Dict[str, int]:
"""
Returns the sorted mapping from string to index. The added tokens encoder is cached for performance
optimisation in `self._added_tokens_encoder` for the slow tokenizers.
Returns:
A dict, the added tokens.
"""
return {k.content: v for v, k in sorted(self.added_tokens_decoder.items(), key=lambda item: item[0])}
@property
def added_tokens_decoder(self) -> Dict[int, AddedToken]:
"""
Returns the added tokens in the vocabulary as a dictionary of index to AddedToken.
Returns:
A dict, the added tokens.
"""
return self._tokenizer.get_added_tokens_decoder()
[docs] def get_added_vocab(self) -> Dict[str, int]:
"""
Returns the added tokens in the vocabulary as a dictionary of token to index.
Returns:
A dict, the added tokens.
"""
return {k.content: v for v, k in sorted(self.added_tokens_decoder.items(), key=lambda item: item[0])}
def __len__(self) -> int:
"""
Size of the full vocabulary with the added tokens.
"""
return self._tokenizer.get_vocab_size(with_added_tokens=True)
@property
def backend_tokenizer(self) -> TokenizerFast:
"""
`tokenizers.implementations.PreTrainedTokenizerBase`, The Rust tokenizer used as a backend.
"""
return self._tokenizer
@property
def decoder(self) -> DecoderFast:
"""
`tokenizers.decoders.Decoder`, The Rust decoder for this tokenizer.
"""
return self._tokenizer.decoder
def _convert_encoding(
self,
encoding: EncodingFast,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
) -> Tuple[Dict[str, Any], List[EncodingFast]]:
"""
Convert the encoding representation (from low-level HuggingFace tokenizer output) to a python Dict and a list
of encodings, take care of building a batch from overflowing tokens.
Overflowing tokens are converted to additional examples (like batches) so the output values of the dict are
lists (overflows) of lists (tokens).
Output shape: (overflows, sequence length)
"""
if return_token_type_ids is None:
return_token_type_ids = "token_type_ids" in self.model_input_names
if return_attention_mask is None:
return_attention_mask = "attention_mask" in self.model_input_names
if return_overflowing_tokens and encoding.overflowing is not None:
encodings = [encoding] + encoding.overflowing
else:
encodings = [encoding]
encoding_dict = defaultdict(list)
for e in encodings:
encoding_dict["input_ids"].append(e.ids)
if return_token_type_ids:
encoding_dict["token_type_ids"].append(e.type_ids)
if return_attention_mask:
encoding_dict["attention_mask"].append(e.attention_mask)
if return_special_tokens_mask:
encoding_dict["special_tokens_mask"].append(e.special_tokens_mask)
if return_offsets_mapping:
encoding_dict["offset_mapping"].append(e.offsets)
if return_length:
encoding_dict["length"].append(len(e.ids))
return encoding_dict, encodings
[docs] def convert_tokens_to_ids(self, tokens: Union[str, List[str]]) -> Union[int, List[int]]:
"""
Converts a token string (or a sequence of tokens) in a single integer id (or a sequence of ids), using the
vocabulary.
Args:
tokens (Union[str, List[str]]): One or several token(s) to convert to token id(s).
Returns:
`int` or `List[int]`, The token id or list of token ids.
"""
if tokens is None:
return None
if isinstance(tokens, str):
return self._convert_token_to_id_with_added_voc(tokens)
return [self._convert_token_to_id_with_added_voc(token) for token in tokens]
def _convert_token_to_id_with_added_voc(self, token: str) -> int:
index = self._tokenizer.token_to_id(token)
if index is None:
return self.unk_token_id
return index
def _convert_id_to_token(self, index: int) -> Optional[str]:
return self._tokenizer.id_to_token(int(index))
def _add_tokens(self, new_tokens: List[Union[str, AddedToken]], special_tokens=False) -> int:
if special_tokens:
self.reset_special_tokens_cache()
return self._tokenizer.add_special_tokens(new_tokens)
return self._tokenizer.add_tokens(new_tokens)
[docs] def num_special_tokens_to_add(self, pair: bool = False) -> int:
"""
Returns the number of added tokens when encoding a sequence with special tokens.
Note:
This encodes a dummy input and checks the number of added tokens, and is therefore not efficient.
Do not put this inside your training loop.
Args:
pair (bool, optional): Whether the number of added tokens should be computed in the case of
a sequence pair or a single sequence. Default: ``False``.
Returns:
`int`, Number of special tokens added to sequences.
"""
return self._tokenizer.num_special_tokens_to_add(pair)
[docs] def convert_ids_to_tokens(
self, ids: Union[int, List[int]], skip_special_tokens: bool = False
) -> Union[str, List[str]]:
"""
Converts a single index or a sequence of indices in a token or a sequence of tokens,
using the vocabulary and added tokens.
Args:
ids (Union[int, List[int]]):
The token id (or token ids) to convert to tokens.
skip_special_tokens (bool, optional):
Whether to remove special tokens in the decoding. Default: ``False``.
Returns:
`str` or `List[str]`, The decoded token(s).
"""
if isinstance(ids, int):
return self._tokenizer.id_to_token(ids)
tokens = []
for index in ids:
index = int(index)
if skip_special_tokens and index in self.all_special_ids:
continue
tokens.append(self._tokenizer.id_to_token(index))
return tokens
def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False, **kwargs) -> List[str]:
return self.encode_plus(text=text, text_pair=pair, add_special_tokens=add_special_tokens, **kwargs).tokens()
[docs] def set_truncation_and_padding(
self,
padding_strategy: PaddingStrategy,
truncation_strategy: TruncationStrategy,
max_length: int,
stride: int,
pad_to_multiple_of: Optional[int],
):
"""
Define the truncation and the padding strategies for fast tokenizers (provided by HuggingFace tokenizers
library) and restore the tokenizer settings after.
The provided tokenizer has no padding / truncation strategy before the managed section. If your tokenizer set a
padding / truncation strategy before, then it will be reset to no padding / truncation when exiting the managed
section.
Args:
padding_strategy (PaddingStrategy):
The kind of padding that will be applied to the input
truncation_strategy (TruncationStrategy):
The kind of truncation that will be applied to the input
max_length (int):
The maximum size of a sequence.
stride (int):
The stride to use when handling overflow.
pad_to_multiple_of (int, optional):
If set will pad the sequence to a multiple of the provided value. This is especially useful
to enable the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
Default: ``None``.
"""
truncation = self._tokenizer.truncation
padding = self._tokenizer.padding
# Set truncation and padding on the backend tokenizer
if truncation_strategy == TruncationStrategy.DO_NOT_TRUNCATE:
if truncation is not None:
self._tokenizer.no_truncation()
else:
target = {
"max_length": max_length,
"stride": stride,
"strategy": truncation_strategy.value,
"direction": self.truncation_side,
}
# _truncation might contain more keys that the target `transformers`
# supports. Use only the target keys to trigger `enable_truncation`.
# This should enable this code to works on various `tokenizers`
# targets.
if truncation is None:
current = None
else:
current = {k: truncation.get(k, None) for k in target}
if current != target:
self._tokenizer.enable_truncation(**target)
if padding_strategy == PaddingStrategy.DO_NOT_PAD:
if padding is not None:
self._tokenizer.no_padding()
else:
length = max_length if padding_strategy == PaddingStrategy.MAX_LENGTH else None
target = {
"length": length,
"direction": self.padding_side,
"pad_id": self.pad_token_id,
"pad_token": self.pad_token,
"pad_type_id": self.pad_token_type_id,
"pad_to_multiple_of": pad_to_multiple_of,
}
if padding != target:
self._tokenizer.enable_padding(**target)
def _batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput], List[TextInputPair], List[PreTokenizedInput], List[PreTokenizedInputPair]
],
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
is_split_into_words: bool = False,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[str] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs
) -> BatchEncoding:
if not isinstance(batch_text_or_text_pairs, (tuple, list)):
raise TypeError(
f"batch_text_or_text_pairs has to be a list or a tuple (got {type(batch_text_or_text_pairs)})"
)
# Set the truncation and padding strategy and restore the initial configuration
self.set_truncation_and_padding(
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
)
encodings = self._tokenizer.encode_batch(
batch_text_or_text_pairs,
add_special_tokens=add_special_tokens,
is_pretokenized=is_split_into_words,
)
# Convert encoding to dict
# `Tokens` has type: Tuple[
# List[Dict[str, List[List[int]]]] or List[Dict[str, 2D-Tensor]],
# List[EncodingFast]
# ]
# with nested dimensions corresponding to batch, overflows, sequence length
tokens_and_encodings = [
self._convert_encoding(
encoding=encoding,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
)
for encoding in encodings
]
# Convert the output to have dict[list] from list[dict] and remove the additional overflows dimension
# From (variable) shape (batch, overflows, sequence length) to ~ (batch * overflows, sequence length)
# (we say ~ because the number of overflow varies with the example in the batch)
#
# To match each overflowing sample with the original sample in the batch
# we add an overflow_to_sample_mapping array (see below)
sanitized_tokens = {}
for key in tokens_and_encodings[0][0].keys():
stack = [
e
for item, _ in tokens_and_encodings
for e in item[key]
]
sanitized_tokens[key] = stack
sanitized_encodings = [e
for _, item in tokens_and_encodings
for e in item]
# If returning overflowing tokens, we need to return a mapping
# from the batch idx to the original sample
if return_overflowing_tokens:
overflow_to_sample_mapping = []
for i, (toks, _) in enumerate(tokens_and_encodings):
overflow_to_sample_mapping += [i] * len(toks["input_ids"])
sanitized_tokens["overflow_to_sample_mapping"] = overflow_to_sample_mapping
for input_ids in sanitized_tokens.get("input_ids"):
self._eventual_warn_about_too_long_sequence(input_ids, max_length, verbose)
return BatchEncoding(sanitized_tokens, sanitized_encodings, tensor_type=return_tensors)
def _encode_plus(
self,
text: Union[TextInput, PreTokenizedInput],
text_pair: Optional[Union[TextInput, PreTokenizedInput]] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
max_length: Optional[int] = None,
stride: int = 0,
is_split_into_words: bool = False,
pad_to_multiple_of: Optional[int] = None,
return_tensors: Optional[bool] = None,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
batched_input = [(text, text_pair)] if text_pair else [text]
batched_output = self._batch_encode_plus(
batched_input,
is_split_into_words=is_split_into_words,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to_multiple_of,
return_tensors=return_tensors,
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**kwargs,
)
# Return tensor is None, then we can remove the leading batch axis
# Overflowing tokens are returned as a batch of output so we keep them in this case
if return_tensors is None and not return_overflowing_tokens:
batched_output = BatchEncoding(
{
key: value[0] if value and isinstance(value[0], list) else value
for key, value in batched_output.items()
},
batched_output.encodings,
)
self._eventual_warn_about_too_long_sequence(batched_output["input_ids"], max_length, verbose)
return batched_output
def convert_tokens_to_string(self, tokens: List[str]) -> str:
return self.backend_tokenizer.decoder.decode(tokens)
def _decode(
self,
token_ids: Union[int, List[int]],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = None,
**kwargs,
) -> str:
self._decode_use_source_tokenizer = kwargs.pop("use_source_tokenizer", False)
if isinstance(token_ids, int):
token_ids = [token_ids]
text = self._tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)
clean_up_tokenization_spaces = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
clean_text = self.clean_up_tokenization(text)
return clean_text
return text
def _save_pretrained(
self,
save_directory: Union[str, os.PathLike],
file_names: Tuple[str],
legacy_format: Optional[bool] = None,
filename_prefix: Optional[str] = None,
) -> Tuple[str]:
"""
Save a tokenizer using the slow-tokenizer/legacy format: vocabulary + added tokens as well as in a unique JSON
file containing {config + vocab + added-tokens}.
"""
save_directory = str(save_directory)
if self.slow_tokenizer_class is None and legacy_format is True:
raise ValueError(
"Your tokenizer does not have a legacy version defined and therefore cannot register this version. You"
" might consider leaving the legacy_format at `None` or setting it to `False`."
)
save_slow = (
(legacy_format is None or legacy_format is True)
and self.slow_tokenizer_class is not None
and self.can_save_slow_tokenizer
)
save_fast = legacy_format is None or legacy_format is False
if save_slow:
added_tokens_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + ADDED_TOKENS_FILE
)
# make sure to be forward compatible
added_vocab = {tok: index for tok, index in self.added_tokens_encoder.items() if index >= self.vocab_size}
if added_vocab:
flags_ = os.O_WRONLY | os.O_CREAT | os.O_TRUNC
with os.fdopen(os.open(added_tokens_file, flags_, FILE_PERMISSION), 'w', encoding="utf-8") as f:
out_str = json.dumps(added_vocab, indent=2, sort_keys=True, ensure_ascii=False) + "\n"
f.write(out_str)
vocab_files = self.save_vocabulary(save_directory, filename_prefix=filename_prefix)
file_names = file_names + vocab_files + (added_tokens_file,)
if save_fast:
tokenizer_file = os.path.join(
save_directory, (filename_prefix + "-" if filename_prefix else "") + TOKENIZER_FILE
)
self.backend_tokenizer.save(tokenizer_file)
file_names = file_names + (tokenizer_file,)
return file_names
[docs] def train_new_from_iterator(
self,
text_iterator,
vocab_size,
length=None,
new_special_tokens=None,
special_tokens_map=None,
**kwargs,
):
"""
Trains a tokenizer on a new corpus with the same defaults (in terms of special tokens or tokenization pipeline)
as the current one.
Args:
text_iterator (list): The training corpus. Should be a generator of batches of texts,
for instance a list of lists of texts if you have everything in memory.
vocab_size (int): The size of the vocabulary you want for your tokenizer.
length (int, optional): The total number of sequences in the iterator. This is used to provide meaningful
progress tracking. Default: ``None``.
new_special_tokens (Union[list, AddedToken], optional): A list of new special tokens to add to the
tokenizer you are training. Default: ``None``.
special_tokens_map (dict, optional): If you want to rename some of the special tokens this
tokenizer uses, pass along a mapping old special token name to new special token name in this argument.
Default: ``None``.
kwargs (Any, optional): Additional keyword arguments.
Returns:
`PreTrainedTokenizerFast`, A new tokenizer of the same type as the original one, trained on
`text_iterator`.
"""
tokenizer_json = json.loads(self._tokenizer.to_str())
# Remove added tokens for now (uses IDs of tokens)
added_tokens = tokenizer_json.pop("added_tokens")
# Remove post processor for now (uses IDs of tokens)
post_processor = tokenizer_json.pop("post_processor")
unk_token = None
# Remove vocab
if tokenizer_json["model"]["type"] == "BPE":
tokenizer_json["model"]["vocab"] = {}
tokenizer_json["model"]["merges"] = []
elif tokenizer_json["model"]["type"] == "Unigram":
if tokenizer_json["model"]["unk_id"] is not None:
unk_id = tokenizer_json["model"]["unk_id"]
unk_token = tokenizer_json["model"]["vocab"][unk_id][0]
if special_tokens_map is not None and unk_token in special_tokens_map:
unk_token = special_tokens_map[unk_token]
tokenizer_json["model"]["unk_id"] = 0
tokenizer_json["model"]["vocab"] = [[unk_token, 0.0]]
elif tokenizer_json["model"]["type"] in ["WordLevel", "WordPiece"]:
tokenizer_json["model"]["vocab"] = {}
else:
raise ValueError(
f"This method does not support this type of tokenizer (found {tokenizer_json['model']['type']}) "
"only BPE, Unigram, WordLevel and WordPiece."
)
if (
special_tokens_map is not None
and "unk_token" in tokenizer_json["model"]
and tokenizer_json["model"]["unk_token"] in special_tokens_map
):
tokenizer_json["model"]["unk_token"] = special_tokens_map[tokenizer_json["model"]["unk_token"]]
tokenizer = TokenizerFast.from_str(json.dumps(tokenizer_json))
# Get the special tokens from the current tokenizer if none are specified.
special_tokens = []
for added_token in added_tokens:
special = added_token.pop("special", None)
_ = added_token.pop("id", None)
if tokenizer_json["model"]["type"] != "Unigram" and not special:
continue
if special_tokens_map is not None and added_token["content"] in special_tokens_map:
added_token["content"] = special_tokens_map[added_token["content"]]
special_tokens.append(AddedToken(**added_token))
if new_special_tokens is not None:
special_tokens.extend(new_special_tokens)
# Trainer needs to know the end of word / continuing subword thingies in BPE
if (
tokenizer_json["model"]["type"] == "BPE"
and "continuing_subword_prefix" not in kwargs
and tokenizer_json["model"]["continuing_subword_prefix"] is not None
):
kwargs["continuing_subword_prefix"] = tokenizer_json["model"]["continuing_subword_prefix"]
if (
tokenizer_json["model"]["type"] == "BPE"
and "end_of_word_suffix" not in kwargs
and tokenizer_json["model"]["end_of_word_suffix"] is not None
):
kwargs["end_of_word_suffix"] = tokenizer_json["model"]["end_of_word_suffix"]
if tokenizer_json["model"]["type"] == "Unigram" and unk_token is not None:
kwargs["unk_token"] = unk_token
if tokenizer_json["pre_tokenizer"] is not None and tokenizer_json["pre_tokenizer"]["type"] == "ByteLevel":
kwargs["initial_alphabet"] = pre_tokenizers_fast.ByteLevel.alphabet()
trainer_class = MODEL_TO_TRAINER_MAPPING[tokenizer_json["model"]["type"]]
trainer = trainer_class(vocab_size=vocab_size, special_tokens=special_tokens, **kwargs)
tokenizer.train_from_iterator(text_iterator, length=length, trainer=trainer)
if post_processor is not None:
trained_tokenizer_json = json.loads(tokenizer.to_str())
# Almost done, we just have to adjust the token IDs in the post processor
if "special_tokens" in post_processor:
for key in post_processor["special_tokens"]:
tokens = post_processor["special_tokens"][key]["tokens"]
if special_tokens_map is not None:
tokens = [special_tokens_map.get(token, token) for token in tokens]
post_processor["special_tokens"][key]["tokens"] = tokens
post_processor["special_tokens"][key]["ids"] = [tokenizer.token_to_id(token) for token in tokens]
for special_token in ["cls", "sep"]:
if special_token in post_processor:
token, _ = post_processor[special_token]
if special_tokens_map is not None and token in special_tokens_map:
token = special_tokens_map[token]
token_id = tokenizer.token_to_id(token)
post_processor[special_token] = [token, token_id]
trained_tokenizer_json["post_processor"] = post_processor
tokenizer = TokenizerFast.from_str(json.dumps(trained_tokenizer_json))
kwargs = self.init_kwargs.copy()
# Map pad/cls/mask token at the Transformers level
special_tokens_list = SpecialTokensMixin.SPECIAL_TOKENS_ATTRIBUTES.copy()
special_tokens_list.remove("additional_special_tokens")
for token in special_tokens_list:
# Get the private one to avoid unnecessary warnings.
if getattr(self, f"_{token}") is not None:
special_token = getattr(self, token)
if special_tokens_map is not None and special_token in special_tokens_map:
special_token = special_tokens_map[special_token]
special_token_full = getattr(self, f"_{token}")
if isinstance(special_token_full, AddedToken):
# Create an added token with the same parameters except the content
kwargs[token] = AddedToken(
special_token,
single_word=special_token_full.single_word,
lstrip=special_token_full.lstrip,
rstrip=special_token_full.rstrip,
normalized=special_token_full.normalized,
special=True,
)
else:
kwargs[token] = special_token
additional_special_tokens = self.additional_special_tokens
if new_special_tokens is not None:
additional_special_tokens.extend(new_special_tokens)
if additional_special_tokens:
kwargs["additional_special_tokens"] = additional_special_tokens
return self.__class__(tokenizer_object=tokenizer, **kwargs)
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
"""
Save only the vocabulary of the tokenizer (vocabulary + added tokens).
This method won't save the configuration and special token mappings of the tokenizer. Use
[`~PreTrainedTokenizerFast._save_pretrained`] to save the whole state of the tokenizer.
Args:
save_directory (str):
The directory in which to save the vocabulary.
filename_prefix (str, optional):
An optional prefix to add to the named of the saved files. Default: ``None``.
Returns:
`Tuple(str)`, Paths to the files saved.
"""
raise NotImplementedError